Upload PPO LunarLander-v2 trained agent

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+replay.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 293.22 +/- 13.34
+      name: mean_reward
+      verified: false
+---
+
+# **PPO** Agent playing **LunarLander-v2**
+This is a trained model of a **PPO** agent playing **LunarLander-v2**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    Policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7843b3877240>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7843b38772e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7843b3877380>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7843b3877420>", "_build": "<function ActorCriticPolicy._build at 0x7843b38774c0>", "forward": "<function ActorCriticPolicy.forward at 0x7843b3877560>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7843b3877600>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7843b38776a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7843b3877740>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7843b38777e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7843b3877880>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7843b3877920>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7843b39f9dc0>"}, "verbose": 1, "policy_kwargs": {"net_arch": {"pi": [256, 256, 256], "vf": [256, 256, 256]}}, "num_timesteps": 2031616, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1754548253967125244, "learning_rate": 0.0001804343, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVhAAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksQhZSMAUOUdJRSlC4="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 496, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "gAWVdwIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBNudW1weS5fY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QolggAAAAAAAAAAQEBAQEBAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUjAFDlHSUUpSMDWJvdW5kZWRfYWJvdmWUaBEolggAAAAAAAAAAQEBAQEBAQGUaBVLCIWUaBl0lFKUjAZfc2hhcGWUSwiFlIwDbG93lGgRKJYgAAAAAAAAAAAAtMIAALTCAACgwAAAoMDbD0nAAACgwAAAAIAAAACAlGgLSwiFlGgZdJRSlIwEaGlnaJRoESiWIAAAAAAAAAAAALRCAAC0QgAAoEAAAKBA2w9JQAAAoEAAAIA/AACAP5RoC0sIhZRoGXSUUpSMCGxvd19yZXBylIxbWy05MC4gICAgICAgIC05MC4gICAgICAgICAtNS4gICAgICAgICAtNS4gICAgICAgICAtMy4xNDE1OTI3ICAtNS4KICAtMC4gICAgICAgICAtMC4gICAgICAgXZSMCWhpZ2hfcmVwcpSMU1s5MC4gICAgICAgIDkwLiAgICAgICAgIDUuICAgICAgICAgNS4gICAgICAgICAzLjE0MTU5MjcgIDUuCiAgMS4gICAgICAgICAxLiAgICAgICBdlIwKX25wX3JhbmRvbZROdWIu", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV3AAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFm51bXB5Ll9jb3JlLm11bHRpYXJyYXmUjAZzY2FsYXKUk5SMBW51bXB5lIwFZHR5cGWUk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJDCAQAAAAAAAAAlIaUUpSMBXN0YXJ0lGgIaA5DCAAAAAAAAAAAlIaUUpSMBl9zaGFwZZQpjAVkdHlwZZRoDowKX25wX3JhbmRvbZROdWIu", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "n_steps": 2048, "gamma": 0.999030148, "gae_lambda": 0.9478055, "ent_coef": 0.00020749, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 8, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWV1gIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwiVAZcAiQFTAJROhZQpjAFflIWUjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjExL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUS4RDCPiAANgPEogKlEMAlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTEvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpRoAIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCF9lH2UKGgYjARmdW5jlIwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBmMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHPyemXtXMm2eFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="}, "system_info": {"OS": "Linux-6.1.123+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Mar 30 16:01:29 UTC 2025", "Python": "3.11.13", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.6.0+cu124", "GPU Enabled": "False", "Numpy": "2.0.2", "Cloudpickle": "3.1.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f299d8769a12eff8cd2560061e45f5ae6623ba11ea0bc63cd772fe2e7d9a9063
+size 3263538

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

ppo-LunarLander-v2/data

@@ -0,0 +1,112 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    Policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7843b3877240>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7843b38772e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7843b3877380>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7843b3877420>",
+        "_build": "<function ActorCriticPolicy._build at 0x7843b38774c0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7843b3877560>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7843b3877600>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7843b38776a0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7843b3877740>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7843b38777e0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7843b3877880>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7843b3877920>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7843b39f9dc0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        "net_arch": {
+            "pi": [
+                256,
+                256,
+                256
+            ],
+            "vf": [
+                256,
+                256,
+                256
+            ]
+        }
+    },
+    "num_timesteps": 2031616,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1754548253967125244,
+    "learning_rate": 0.0001804343,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVhAAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksQhZSMAUOUdJRSlC4="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.015808000000000044,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 496,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "gAWVdwIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBNudW1weS5fY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QolggAAAAAAAAAAQEBAQEBAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUjAFDlHSUUpSMDWJvdW5kZWRfYWJvdmWUaBEolggAAAAAAAAAAQEBAQEBAQGUaBVLCIWUaBl0lFKUjAZfc2hhcGWUSwiFlIwDbG93lGgRKJYgAAAAAAAAAAAAtMIAALTCAACgwAAAoMDbD0nAAACgwAAAAIAAAACAlGgLSwiFlGgZdJRSlIwEaGlnaJRoESiWIAAAAAAAAAAAALRCAAC0QgAAoEAAAKBA2w9JQAAAoEAAAIA/AACAP5RoC0sIhZRoGXSUUpSMCGxvd19yZXBylIxbWy05MC4gICAgICAgIC05MC4gICAgICAgICAtNS4gICAgICAgICAtNS4gICAgICAgICAtMy4xNDE1OTI3ICAtNS4KICAtMC4gICAgICAgICAtMC4gICAgICAgXZSMCWhpZ2hfcmVwcpSMU1s5MC4gICAgICAgIDkwLiAgICAgICAgIDUuICAgICAgICAgNS4gICAgICAgICAzLjE0MTU5MjcgIDUuCiAgMS4gICAgICAgICAxLiAgICAgICBdlIwKX25wX3JhbmRvbZROdWIu",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_shape": [
+            8
+        ],
+        "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWV3AAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFm51bXB5Ll9jb3JlLm11bHRpYXJyYXmUjAZzY2FsYXKUk5SMBW51bXB5lIwFZHR5cGWUk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJDCAQAAAAAAAAAlIaUUpSMBXN0YXJ0lGgIaA5DCAAAAAAAAAAAlIaUUpSMBl9zaGFwZZQpjAVkdHlwZZRoDowKX25wX3JhbmRvbZROdWIu",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "n_steps": 2048,
+    "gamma": 0.999030148,
+    "gae_lambda": 0.9478055,
+    "ent_coef": 0.00020749,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 8,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWV1gIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwiVAZcAiQFTAJROhZQpjAFflIWUjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjExL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUS4RDCPiAANgPEogKlEMAlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTEvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpRoAIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCF9lH2UKGgYjARmdW5jlIwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBmMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHP8Ci4NWYTWmFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:500190dd3919481f83759c23d8a9fd543b0a364c630eecb0c6ebd886848fcba6
+size 2165382

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4558c5cd28a4d77ba9cc6a09f9e8ce38340c996018c8d1a006b97b60a78f82a5
+size 1082022

ppo-LunarLander-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-6.1.123+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Mar 30 16:01:29 UTC 2025
+- Python: 3.11.13
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.6.0+cu124
+- GPU Enabled: False
+- Numpy: 2.0.2
+- Cloudpickle: 3.1.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eaef91df343082a54e239e539dc5346a4e14db06c4521974ede46c997f47c760
+size 149828

results.json

@@ -0,0 +1 @@
+{"mean_reward": 293.21956107174617, "std_reward": 13.340216583930031, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-08-07T07:40:10.280847"}